This document discusses different types of data and variables. There are four main types of variables based on their level of measurement: nominal, ordinal, interval, and ratio. Nominal variables consist of categories that cannot be ranked, while ordinal variables can be ranked but the distance between categories is unknown. Interval and ratio variables are measured on a continuous scale, with interval lacking a true zero point and ratio having a true zero. The level of measurement affects what statistical analyses can be performed. Knowing the data type is important for research design and analysis.

1. Numeracy & Quantitative Methods:
Numeracy for Professional Purposes
Laura Lake

2. •When collecting or gathering data we collect data from
individuals cases on particular variables.
• A variable is a unit of data collection whose value can vary.
• Variables can be defined into types according to the level of
mathematical scaling that can be carried out on the data.
• There are four types of data or levels of measurement:
Introduction
Introduction
1. Nominal 2. Ordinal
3. Interval 4. Ratio

3. • Nominal or categorical data is data that comprises of categories
that cannot be rank ordered – each category is just different.
•The categories available cannot be placed in any order and no
judgement can be made about the relative size or distance from
one category to another.
•What does this mean? No mathematical operations can be
performed on the data relative to each other.
•Therefore, nominal data reflect qualitative differences rather
than quantitative ones.
Nominal data
Nominal data

4. Examples:
Nominal data
Nominal data
What is your gender?
(please tick)
Male
Female
Did you enjoy the
film? (please tick)
Yes
No

5. •Systems for measuring nominal data must ensure that each
category is mutually exclusive and the system of
measurement needs to be exhaustive.
• Variables that have only two responses i.e.Yes or No, are
known as dichotomies.
Nominal data
Nominal data

6. • Ordinal data is data that comprises of categories that can be
rank ordered.
• Similarly with nominal data the distance between each
category cannot be calculated but the categories can be
ranked above or below each other.
•What does this mean? Can make statistical judgements and
perform limited maths.
Ordinal data
Ordinal data

7. Example:
Ordinal data
Ordinal data
How satisfied are you with the level of
service you have received? (please tick)
Very satisfied
Somewhat satisfied
Neutral
Somewhat dissatisfied
Very dissatisfied

8. • Both interval and ratio data are examples of scale data.
• Scale data:
• data is in numeric format (£50, £100, £150)
•data that can be measured on a continuous scale
• the distance between each can be observed and as a
result measured
• the data can be placed in rank order.
Interval and ratio data
Interval and ratio data

9. • Interval data measured on a continuous scale and has no
true zero point.
• Examples:
•Time – moves along a continuous measure or seconds,
minutes and so on and is without a zero point of time.
•Temperature – moves along a continuous measure of
degrees and is without a true zero.
Interval data
Interval data

10. • Ratio data measured on a continuous scale and does have a
true zero point.
• Examples:
• Age
•Weight
• Height
Ratio data
Ratio data

11. •These levels of measurement can be placed in hierarchical
order.
Hierarchical data order
Hierarchical data order
Ratio
Interval
Ordinal
Nominal

12. • Nominal data is the least complex and give a simple measure
of whether objects are the same or different.
• Ordinal data maintains the principles of nominal data but
adds a measure of order to what is being observed.
• Interval data builds on ordinal by adding more information
on the range between each observation by allowing us to
measure the distance between objects.
• Ratio data adds to interval with including an absolute zero.
Hierarchical data order
Hierarchical data order

13. • Knowing the hierarchy of data is useful.
•Why? It is possible to recode or adjust certain types of data
into others.
• Can go from most complex (interval and ratio) to least
complex (nominal) but cannot go the other way around.
• Interval/ratio can be re-formatted to become ordinal or
nominal, ordinal can become nominal.
Hierarchical data order
Hierarchical data order

14. • Example: salary data for is often recorded as interval data
(i.e. just a number).
•Why? Because it can then be analysed in many ways:
• Any mathematical operation e.g. average salary
• reformatted into ordinal or nominal data e.g. salary
bands (£10,000 to £14,999, £15,000 to £19,999)
Hierarchical data order
Hierarchical data order

15. • If salary data is collected as an ordinal variable i.e. in salary
bands, then it becomes impossible to perform mathematical
operations such as finding the average salary.
• So, if possible data such as this should be collected as scale
data and these issues should be thought about at the research
design stage.
Hierarchical data order
Hierarchical data order

16. • Why do we need to know what type of data we are dealing
with?
•The data type or level of measurement influences the type of
statistical analysis techniques that can be used when
analysing data.
• See the next set of lectures on descriptive statistics.
Data types
Data types –
– important?
important?

17. Bryman,A. (2008) Social Research Methods. 3rd Ed. Oxford: Oxford
University Press.
David, M. and Sutton, C. (2011) Social Research : An Introduction.
2nd ed. London: Sage.
References
References

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Author Laura Lake
Institute University of Plymouth
Title
Numeracy & Quantitative Methods
Numeracy for Professional Purposes
Description Overview of data types
Date Created May 2011
Educational Level Level 4
Keywords
Variable, nominal, ordinal, interval, ratio, mutually exclusive, exhaustive,
hierarchical order, dichotomies.
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